Individual Identification Using Cognitive Electroencephalographic Neurodynamics

Byoung-Kyong Min, Heung Il Suk, Min Hee Ahn, Min Ho Lee, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


As the brain is a unique biological system that reflects the subtle distinctions in the mental attributes of individual humans, electroencephalographic (EEG) signals have been regarded as one of the most promising and potent biometric signals for discriminating between individuals. However, existing EEG-based user-recognition methods present only a limited range of individual distinctions. In this paper, we propose a novel system of decoding cognitive EEG signals for individual identification with high accuracy. Specifically, we investigate the feasibility of our system, which can recognize an individual based on the discriminative patterns of source-level causal connectivity among brain regions, estimated from scalp-level EEG signals. The EEG signals were produced by a steady-state visual evoked potential-inducing grid-shaped top-down paradigm. This system can, in principle, use top-down cognitive features analyzed by individuals' differently characterized neurodynamic causal connectivities. In this paper, we achieved a maximal accuracy of 98.60% on average in 20 subjects, for whom we estimated causal connectivity in 16 brain regions using 5-s intervals of EEG signals. Our system shows promising initial results toward building a practical identification technology able to recognize individuals by means of brain neurodynamics.

Original languageEnglish
Article number7915720
Pages (from-to)2159-2167
Number of pages9
JournalIEEE Transactions on Information Forensics and Security
Issue number9
Publication statusPublished - 2017 Sept


  • Electroencephalography
  • causality
  • cognitive system
  • identification
  • support vector machine
  • top-down processing

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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